Mobile parcel lockers with individual customer service

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed


The ongoing growth of e-commerce deliveries has led to a significant increase in last-mile delivery volumes. New technologies are being investigated to provide these deliveries efficiently and in a customer-friendly manner. A common practice is to use fixed parcel lockers (FPLs) to make deliveries independent from the presence of the customer as is the case in attended home deliveries (AHDs). FPLs are usually installed at key locations in cities, and customers can collect their package at any time once it has been delivered to this particular location. Mobile parcel lockers (MPLs) represent a new idea: they can be parked for temporary collection of items at different locations, keeping the pickup distance to the customer short and avoiding high infrastructure costs. However, customers need to collect their parcels within a restricted time window. This service is supposed to become especially efficient through autonomously operating vehicles that can move MPLs at low costs. In this article, we introduce the heterogeneous locker location problem to study the effects of fleets combining two of the services—FPLs, MPLs, AHDs—within one framework. In our comparison, we consider that customers may have different expectations regarding their maximum pickup distance as well as their temporal flexibility in accepting deliveries. A fixed fleet is applied to maximize the number of customers served, respecting individual customer preferences in terms of pickup distances and time windows. We evaluate the different delivery services regarding managerial insights on service quality and efficiency. In the experiments, we analyze the impact of structural demand differences and different fleet sizes, as well as the operational fleet utilization and the individual customer experience. Results show the potential to increase the number of customers served by about 14%–19% through the use of MPLs while considering individual customer preferences.

Seiten (von - bis)506-526
Frühes Online-Datum28 Juli 2023
PublikationsstatusVeröffentlicht - Dez. 2023

ÖFOS 2012

  • 101015 Operations Research